本文整理汇总了Python中doom_py.DoomGame.make_action方法的典型用法代码示例。如果您正苦于以下问题：Python DoomGame.make_action方法的具体用法？Python DoomGame.make_action怎么用？Python DoomGame.make_action使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类doom_py.DoomGame的用法示例。
在下文中一共展示了DoomGame.make_action方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: DoomEnv
# 需要导入模块: from doom_py import DoomGame [as 别名]
# 或者: from doom_py.DoomGame import make_action [as 别名]
#.........这里部分代码省略.........
return
def _play_human_mode(self):
while not self.game.is_episode_finished():
self.game.advance_action()
state = self.game.get_state()
total_reward = self.game.get_total_reward()
info = self._get_game_variables(state.game_variables)
info["TOTAL_REWARD"] = round(total_reward, 4)
print('===============================')
print('State: #' + str(state.number))
print('Action: \t' + str(self.game.get_last_action()) + '\t (=> only allowed actions)')
print('Reward: \t' + str(self.game.get_last_reward()))
print('Total Reward: \t' + str(total_reward))
print('Variables: \n' + str(info))
sleep(0.02857) # 35 fps = 0.02857 sleep between frames
print('===============================')
print('Done')
return
def _step(self, action):
if NUM_ACTIONS != len(action):
logger.warn('Doom action list must contain %d items. Padding missing items with 0' % NUM_ACTIONS)
old_action = action
action = [0] * NUM_ACTIONS
for i in range(len(old_action)):
action[i] = old_action[i]
# action is a list of numbers but DoomGame.make_action expects a list of ints
if len(self.allowed_actions) > 0:
list_action = [int(action[action_idx]) for action_idx in self.allowed_actions]
else:
list_action = [int(x) for x in action]
try:
reward = self.game.make_action(list_action)
state = self.game.get_state()
info = self._get_game_variables(state.game_variables)
info["TOTAL_REWARD"] = round(self.game.get_total_reward(), 4)
if self.game.is_episode_finished():
is_finished = True
return np.zeros(shape=self.observation_space.shape, dtype=np.uint8), reward, is_finished, info
else:
is_finished = False
return state.image_buffer.copy(), reward, is_finished, info
except doom_py.vizdoom.ViZDoomIsNotRunningException:
return np.zeros(shape=self.observation_space.shape, dtype=np.uint8), 0, True, {}
def _reset(self):
if self.is_initialized and not self._closed:
self._start_episode()
return self.game.get_state().image_buffer.copy()
else:
return self._load_level()
def _render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None # If we don't None out this reference pyglet becomes unhappy
return
try:
if 'human' == mode and self.no_render:
return
state = self.game.get_state()
img = state.image_buffer示例2: DoomEnv
# 需要导入模块: from doom_py import DoomGame [as 别名]
# 或者: from doom_py.DoomGame import make_action [as 别名]
#.........这里部分代码省略.........
return
def _play_human_mode(self):
while not self.game.is_episode_finished():
self.game.advance_action()
state = self.game.get_state()
total_reward = self.game.get_total_reward()
info = self._get_game_variables(state.game_variables)
info["TOTAL_REWARD"] = round(total_reward, 4)
print('===============================')
print('State: #' + str(state.number))
print('Action: \t' + str(self.game.get_last_action()) + '\t (=> only allowed actions)')
print('Reward: \t' + str(self.game.get_last_reward()))
print('Total Reward: \t' + str(total_reward))
print('Variables: \n' + str(info))
sleep(0.02857) # 35 fps = 0.02857 sleep between frames
print('===============================')
print('Done')
return
def _step(self, action):
if NUM_ACTIONS != len(action):
logger.warn('Doom action list must contain %d items. Padding missing items with 0' % NUM_ACTIONS)
old_action = action
action = [0] * NUM_ACTIONS
for i in range(len(old_action)):
action[i] = old_action[i]
# action is a list of numbers but DoomGame.make_action expects a list of ints
if len(self.allowed_actions) > 0:
list_action = [int(action[action_idx]) for action_idx in self.allowed_actions]
else:
list_action = [int(x) for x in action]
try:
reward = self.game.make_action(list_action)
state = self.game.get_state()
info = self._get_game_variables(state.game_variables)
info["TOTAL_REWARD"] = round(self.game.get_total_reward(), 4)
if self.game.is_episode_finished():
is_finished = True
return np.zeros(shape=self.observation_space.shape, dtype=np.uint8), reward, is_finished, info
else:
is_finished = False
return state.image_buffer.copy(), reward, is_finished, info
except doom_py.vizdoom.ViZDoomIsNotRunningException:
return np.zeros(shape=self.observation_space.shape, dtype=np.uint8), 0, True, {}
def _reset(self):
if self.is_initialized and not self._closed:
self._start_episode()
image_buffer = self.game.get_state().image_buffer
if image_buffer is None:
raise error.Error(
'VizDoom incorrectly initiated. This is likely caused by a missing multiprocessing lock. ' +
'To run VizDoom across multiple processes, you need to pass a lock when you configure the env ' +
'[e.g. env.configure(lock=my_multiprocessing_lock)], or create and close an env ' +
'before starting your processes [e.g. env = gym.make("DoomBasic-v0"); env.close()] to cache a ' +
'singleton lock in memory.')
return image_buffer.copy()
else:
return self._load_level()
def _render(self, mode='human', close=False):
if close:
if self.viewer is not None: